This article provides an overview of the different categories of mathematical models used for building intrusion detection systems (IDS) to protect computer networks from malicious activities. The models discussed include statistical models, rule-based models, machine learning models, fuzzy logic models, and graph-based models, each with its own unique strengths and weaknesses. The research work compares these models based on various criteria, such as accuracy, precision, F1-score, and false alarm rate, and presents the results in a table format. The article also includes statistics on the usage of these models over time and which models were used in the last year. This information provides valuable insights into the trends in intrusion detection systems and the popularity of different models. Overall, the article serves as a useful resource for researchers and practitioners interested in designing effective IDS for securing computer networks.